bioRxivBiochemistry
Extended snake venomics by top-down in-source decay: Investigating the newly discovered Anatolian Meadow viper subspecies, Vipera anatolica senliki
B.-F. Hempel, M. Damm, M. Mrinalini, B. Göcmen, M. Karis, A. Nalbantsoy, R. M. Kini, R. D. Süssmuth
Abstract
Herein we report on the venom proteome of Vipera anatolica senliki, a recently discovered and hitherto unexplored subspecies of the critically endangered Anatolian Meadow viper endemic to the Antalya Province of Turkey. Integrative venomics, including venom gland transcriptomics as well as complementary bottom-up and top-down proteomic analyses, were applied to fully characterize the venom of V. a. senliki. Furthermore, the classical top-down venomics approach was extended to elucidate the venom proteome by an alternative in-source decay (ISD) proteomics workflow using the reducing matrix 1,5-diaminonaphthalene (1,5-DAN). Top-down ISD proteomics allows for disulfide bond mapping as well as effective de novo identification of high molecular weight venom constituents, both of which are difficult to achieve by commonly established top-down approaches. Venom gland transcriptome analysis identified 42 venom transcript annotations from 13 venom toxin families. Relative quantitative snake venomics revealed snake venom metalloproteinases (svMP, 42.9%) as the most abundant protein family, followed by several less dominant toxin families. Online mass profiling and top-down venomics provide a detailed insight into the venom proteome of V. a. senliki and facilitates a comparative analysis of venom variability for the closely related subspecies, V. a. anatolica.\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=60 SRC=\"FIGDIR/small/773606v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (35K):\norg.highwire.dtl.DTLVardef@2e26ecorg.highwire.dtl.DTLVardef@1fd6bbeorg.highwire.dtl.DTLVardef@ac8f25org.highwire.dtl.DTLVardef@e9ea62_HPS_FORMAT_FIGEXP M_FIG TOC Figure C_FIG
- assay: proteomics
- research focus: cell type annotation
- approach: deep learning
- approach: statistical model
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